Wireless communication systems, such as the 3rd Generation (3G) of mobile telephone standards and technology, are well known. An example of such 3G standards and technology is the Universal Mobile Telecommunications System (UMTS™), developed by the 3rd Generation Partnership Project (3GPP™) (www3gpp.org).
The 3rd and 4th generations of wireless communications, and systems such as LTE, have generally been developed to support macro-cell mobile phone communications. Here the ‘phone’ may be a smart phone, or another mobile or portable communication unit that is linked wirelessly to a network, through which calls are connected. Calls may be data, video, or voice calls, or a combination of these. Such macro cells utilise high power base stations to communicate with wireless communication units within a relatively large geographical coverage area. The coverage area may be several square kilometres, or larger if it is not in a built-up area.
Typically, mobile/portable wireless communication units, or User Equipment (UEs) as they are often referred to in 3G, communicate with a Core Network (CN) of the 3G wireless communication system. This communication is via a Radio Network Subsystem (RNS). A wireless communication system typically comprises a plurality of Radio Network Subsystems. Each Radio Network Subsystem comprises one or more cells, to which UEs may attach, and thereby connect to the network. A base station may serve a cell with multiple antennas, each of which serves one sector of the cell.
A parameter of interest to operators of mobile communication networks is ‘quality of service information’. This is information that reveals how well the network is supporting users of the network. A high quality of service may be indicated by a very low rate of ‘dropped’ calls, or by very few mobiles experiencing low or highly variable signal strength.
In most known cellular networks, quality of service information is reported on a ‘per-cell’ or ‘per-sector basis’. This means that the network statistics obtained will only provide an indication of, for example, the average data rate or the average number of dropped calls in a given sector. The ‘per cell’ or ‘per sector’ statistics are usually used since it is possible with prior art arrangements to develop meaningful statistics at the cell or sector level, without excessive data processing being required. These averages do not allow the network operator to narrow down the information, for example, to indicate if a particular portion of the sector is:    (i) Habitually causing calls to be dropped; or    (ii) Suffering from a poor data rate. A poor data rate may arise as a consequence of poor coverage in that particular area, or due to interference from a neighbouring cell.
A more detailed view of these issues is very useful to network operators. One prior art approach is thus to conduct ‘drive tests’, to assess coverage within a sector or cell. A test phone is driven in a vehicle through the cell, to derive individual measurements of e.g. signal strength, at exact locations. However, drive testing is expensive, and only provides data on what is happening at street level along the particular path taken during the test. The majority of phone and data calls are now made within buildings, and drive tests do not give any indication of the quality of service experience within a building. This is a major issue.
Geo-location is the identification of the real-world geographical location of, say, a UE of a 3G system or the like. Geo-location of UEs can be performed in several ways. These include providing a UE with positioning equipment, such as GPS, or using network and mobile measurement data for nearby cells.
However, even if a user's terminal device has a GPS receiver built in, these devices are frequently disabled by users to save battery life. They are not included at all on many devices, e.g. low-cost handsets, data ‘dongles’ for laptops, and machine-to-machine data communication terminals. The use of GPS data alone is therefore not sufficient to build an accurate picture of network service levels.
Some cellular systems are mandated to provide user location information when an emergency (‘911’) call is made. Again these calls are not sufficiently frequent to build up a good picture of the quality of service experienced throughout a network at all times of the day and night and in all seasons of the year. In addition, the network equipment architecture required in these ‘E911 Geolocation’ systems is complex, since every base-station in the network needs to be fitted with an additional piece of electronics in order to locate the user to a suitable (mandated) degree of accuracy, which is typically 100m. To use this type of architecture for service quality assessments throughout a network would be prohibitively expensive.
The network statistics referred to above, which only provide the average data rate or the average number of dropped calls in a given sector, are insufficient for some tasks faced by network operators. For example, if a particular mobile user complains that he is often subject to poor service, the data that is available may not help. Likewise, some individual faults in the network, such as wrongly directed antennas, may not be revealed by the ‘average’ data.
To try and improve the information available on service levels, some operators have attempted to compile more comprehensive data for a limited time on what exactly is happening in one sector or one cell of a network. There are two reasons why this is rarely done, as follows:    (i) A vast amount of data is created, even for a short time period such as a few hours. Storing this data in a retrievable form is very expensive.    (ii) If data concerning calls made in a sector or a cell of a network is captured for a period of a few hours, it then requires specialist post-processing. Any information that can be derived from the data about a user or part of the network is then often only available several hours after the end of the data capture. This may be several days after a user has made a complaint. Such information is only of limited value.
Prior art approaches often amount to a ‘batch processing’ technique. This approach gathers all of the information for a time period after a problem arose in a network sector. The data is essentially obtained manually, by the operator of the network. The operator makes a decision which cell to monitor, and for how long. The cell may be chosen, for example, because it is the cell in which a user who has made a complaint lives. The duration of the monitoring may depend on the amount of storage that the operator considers justified for the investigation. After capture of the data, it is then ‘fed’ to the processing system, which will then process the information off-line. This typically results in a delay of many hours, between the event of interest taking place and the resulting diagnostic information being available.
The fact that the process is ‘retroactive’ may also be a problem. The approach will only assist in identifying a fault:    (i) If it is a network fault that is still detectable, rather than one that only occurs intermittently. An intermittent fault could have many causes, such as one that occurs at particular times of day, or under certain external circumstances such as ambient temperature or vibrations of an antenna due to a particular wind direction.    (ii) If the user happens to be active during the few hours when the data is captured, for faults that are entirely due to the user's handset.
In summary, the prior art typically relies on average statistics for much quality of service analysis. Where data is captured, it is for a limited period of a few hours for a sector or cell where the operator judges that investigation may be warranted, despite the cost of carrying out that investigation. The captured data is often only of value if the fault happens to re-occur during the period of capture. Skilled analysis is required to process the captured data.
FIG. 1 provides a more detailed view of a prior art approach seeking to extract data concerning activity in the network, for a period of a few hours. The data may be obtained from a radio network controller RNC 112, 114 or 116 or similar network element, of a mobile radio communications network 110.
This data may then be processed to provide geographic location information, for the users in a cell or sector, or for a particular user who has reported a fault. The quality of service information may also be calculated for these users, or the particular user. Server 120 might be used to carry out this processing. The amounts of data involved in this process are extremely large.
Prior art systems that do attempt to record all data about what is happening in a sector or cell tend to place this data, in its entirety, on a single large storage device or on a number of large storage devices, associated with the final information database. Such a device is shown as storage device 140, linked to server 120 by a further database server 130. If information is available from a network planning system, it may too be supplied to server 140.
Users 150 are shown at the bottom edge of FIG. 1. The users 150 may access the communication session data from the large storage device 140, via an application server.
The processing in server 120 may be very basic. It may involve simply forwarding large volumes of data to storage device 140. The server may then calculate geolocation data once a user has started to enquire about a particular call, or a particular mobile communications unit in use in network 110. A result can then be returned to the user after:    (i) The original session data has been located within the large volume of data on storage device 140; and    (ii) A geolocation calculation is then performed on the original session data, once it has been found.The delay for a user of the system may be considerable, and the cost of the equipment to provide even this level of service may be very great.
It might be assumed that it is possible to sort the acquired data and ‘throw away’ the information that is not required for display and reporting purposes. However, this has proved unacceptable in prior art systems, because the personnel in the network operations centre may wish to access a wide variety of measurements and parameters. They have a requirement to be able to diagnose problems with particular calls, or particular user devices. This may be necessary, to inform a particular user as to why they continually drop calls. It is hard, in advance, to know which information may not be needed for display. A clear answer has to be provided for particular users about whether, for example, the fault is due either to their own phone being faulty, or the network being at fault, such as due to poor coverage. It may also be necessary to diagnose a systemic fault with a particular brand or model of phone, or other user terminal device.
It has therefore proved necessary to retain all information about every call, in order to provide the desired level of detail to the operations centre staff, but nevertheless to provide near real-time quality of service data at the level of average statistics, for example for a whole cell. These average statistics fulfil the need of operations staff to be able to judge overall network quality, in all parts of their network, in addition to providing more precise data for a particular user or class of handset. It is this prior art approach that the present invention attempts to solve.
When seeking information about, for example, an individual handset or user, the prior art storage and retrieval mechanism centred on server 120 and storage device 140 has significant disadvantages. The main one is that the whole of the data must be searched and processed, in order just to extract the relatively small amounts of data which the network operations centre needs in order to make day-to-day judgements upon the quality of service offered by the network, for its users.
The ‘raw’ information which must be searched amounts to some ten times the amount of data which is typically required for display to the network operations managers. The processing of this unnecessarily large amount of data makes the reporting process very slow. In a typical prior art approach, the reporting process can take many hours. As a consequence, the information is therefore available to the network management centre some significant time after the user's phone-calls or data sessions to which that quality-of-service data refers. The information will sometimes be used for very practical changes. For example, there may be a need to arrange for alterations of antenna downtilt angles, or changes in cell-site transmit power levels, to reduce interference or increase coverage. The significant time delay in obtaining the information necessary to decide on these changes means, in real networks, that any required network changes occur much too late. Customers and/or calls are then lost.